Computers are often used to solve complex quantitative and qualitative problems. For problems that involve a large data set, a specially trained professional, known as a data scientist, is often hired. The data scientist interprets the data set and constructs models that can be processed by computers to solve the problem. However, hiring data scientists is cost prohibitive for many organizations.
For certain types of problems, advanced computing techniques, such as genetic algorithms or backpropagation, may be available to develop a model, such as a neural network, that is comparable in accuracy to a model that would be created by a data scientist. However, genetic algorithms may take a large number of iterations to converge on an acceptable neural network, and backpropagation may be slow when a large data set is being modeled or when the neural network includes a large number of nodes, connections, or layers.
Furthermore, various types of machine-learning problems exist. For example, regression problems involve evaluating a series of inputs to predict a numeric output, classification problems involve evaluating a series of inputs to predict a categorical output, and reinforcement learning involves performing actions within an environment to optimize some notion of a positive reward. Due to the differences in the various types of problems, the available mechanisms to generate and train a neural network or other machine learning solution may be problem-specific. For example, a support vector machine (SVM) may be suitable for some classification problems, logistic regression may be suitable for some regression problems, and a specialized machine learning package, such as TensorFlow, may be suitable for reinforcement learning. Thus, generating and training neural networks that meet performance requirements for each of multiple types of problems faced by an enterprise may be slow and difficult.